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For over 25 years, art museums have digitized their collections and made them available online for broad public access and reuse, tapping citizens to become curators and content creators. Yet, public engagement remains scarce, owing in large part to the lack of necessary social and technical skills among non-expert users to take part in existing digital cultural participation models. While facilitating reuse continues to be seen as an effective engagement strategy, the chapter instead argues that fostering broad public participation towards collective cultural production depends on developing an alternative approach to algorithmic curation to facilitate user-driven exploration and aesthetic choice by means of a novel, interactive recommender system for online collections. To support this claim, the chapter critically examines the parallels between curation and personalization, highlighting both the importance of pre-filtering for user-driven choice, and the drawbacks of current recommender systems when it comes to automating art curation, including their limited ability to sustain discovery and reduce external influences on aesthetic judgment as well as their interference with personal taste. To address these different issues, the chapter turns to a rare physical exhibition making approach, which it coins “creative curating”, as an alternative model for algorithmic curation. Drawing on associative theory, it describes how creative curating diverges from current recommendation methods, which rely on principles of similarity and serendipity, in that it exploits a third associative approach known as mediation to establish complex semantic relationships between different artworks. The chapter concludes by speculating how a novel, interactive recommender system for online art collections powered by this curatorial model could be used to nudge users into making personally indicative choices as they intuitively explore collection contents, thus providing art institutions with actionable data on users’ unique perspective towards power sharing on a mass scale. In doing so, the chapter ultimately advances a new digital cultural participation model based on an unprecedented method to reverse-engineer aesthetic judgment.

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Re-mediating Interaction with Online Art Collections: How Creative Curating Can Enhance Discovery Through Recommendations Towards a New Model of Cultural Participation

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Stéphanie Bertrand,
  • George Papagiannakis,
  • George Margetis,
  • Stavroula Ntoa,
  • Nikolaos Partarakis,
  • Xenophon Zabulis,
  • Constantine Stephanidis

摘要

For over 25 years, art museums have digitized their collections and made them available online for broad public access and reuse, tapping citizens to become curators and content creators. Yet, public engagement remains scarce, owing in large part to the lack of necessary social and technical skills among non-expert users to take part in existing digital cultural participation models. While facilitating reuse continues to be seen as an effective engagement strategy, the chapter instead argues that fostering broad public participation towards collective cultural production depends on developing an alternative approach to algorithmic curation to facilitate user-driven exploration and aesthetic choice by means of a novel, interactive recommender system for online collections. To support this claim, the chapter critically examines the parallels between curation and personalization, highlighting both the importance of pre-filtering for user-driven choice, and the drawbacks of current recommender systems when it comes to automating art curation, including their limited ability to sustain discovery and reduce external influences on aesthetic judgment as well as their interference with personal taste. To address these different issues, the chapter turns to a rare physical exhibition making approach, which it coins “creative curating”, as an alternative model for algorithmic curation. Drawing on associative theory, it describes how creative curating diverges from current recommendation methods, which rely on principles of similarity and serendipity, in that it exploits a third associative approach known as mediation to establish complex semantic relationships between different artworks. The chapter concludes by speculating how a novel, interactive recommender system for online art collections powered by this curatorial model could be used to nudge users into making personally indicative choices as they intuitively explore collection contents, thus providing art institutions with actionable data on users’ unique perspective towards power sharing on a mass scale. In doing so, the chapter ultimately advances a new digital cultural participation model based on an unprecedented method to reverse-engineer aesthetic judgment.